M
AI Engineer (Agentic AI / AWS Bedrock)
MatchPoint Solutions
2 hours ago
Full-time
On-site
Los Angeles, California, United States
MatchPoint Solutions is a fast-growing, young, energetic global
IT-Engineering services company with clients across the US . We provide technology solutions to various clients like
Uber, Robinhood, Netflix, Airbnb, Google, Sephora, and more!
More recently, we have expanded to working internationally in
Canada, China, Ireland, UK, Brazil, and India . Through our culture of innovation, we inspire, build, and deliver business results, from idea to outcome. We keep our clients on the cutting edge of the latest technologies and provide solutions by using industry-specific best practices and expertise.
We are excited to be continuously expanding our team. If you are interested in this position, please send over your updated resume. We look forward to hearing from you!
Job Title:
AI Engineer (Agentic AI / AWS Bedrock)
Location: (Remote / Hybrid / On-site - City, Country) Los Angeles,CA - Hybrid
Employment Type: (Full-time / Part-time / Contract) - Contract
Pay Range: 70-80/hr
We are seeking an experienced
AI Engineer
with strong 6-8 yrs experience. Hands-on expertise in building, deploying, and monitoring production-grade AI agents and Generative AI solutions using AWS technologies.
The ideal candidate will have experience developing AI agents using
Claude Agent SDK , deploying them on
Amazon Bedrock AgentCore Runtime , automating deployments through
GitHub Actions , and implementing
Langfuse for observability and continuous evaluation of agent traffic .
The role requires a strong combination of AI engineering, cloud deployment, data pipeline development, and LLM performance monitoring.
Key Responsibilities
1. Agentic AI Development
Design, develop, and deploy AI agents using Claude Agent SDK and Python. Build intelligent, autonomous agents capable of executing complex workflows and interacting with external tools, APIs, and knowledge bases. Develop agent orchestration workflows with appropriate error handling, retry mechanisms, and guardrails. Integrate AI agents with enterprise applications and data sources. 2. AWS Bedrock & AgentCore
Deploy and manage AI agents using Amazon Bedrock AgentCore Runtime. Develop scalable, secure, and production-ready agent architectures on AWS. Integrate agents with Amazon Bedrock foundation models and enterprise knowledge bases. Implement agent authentication, access controls, logging, and runtime monitoring. 3. Langfuse Deployment & AI Observability
Deploy, configure, and maintain Langfuse for comprehensive LLM and agent observability. Integrate Langfuse with AI agents to capture traces, prompts, responses, tool calls, and execution metrics. Monitor agent performance, response latency, token consumption, and operational costs. Identify and troubleshoot production issues through distributed tracing and monitoring dashboards. 4. Continuous Agent Evaluation
Design and implement continuous evaluation frameworks for production AI agent traffic. Evaluate agent responses for accuracy, relevance, hallucinations, task completion, and tool execution quality. Develop automated evaluation pipelines using Langfuse, LLM-as-a-Judge, and custom evaluation metrics. Implement continuous feedback mechanisms to improve agent reliability and effectiveness. Establish quality benchmarks and detect agent performance regressions. 5. Data Pipelines & Knowledge Base Integration
Develop AWS Step Functions workflows integrating Amazon Textract and Amazon Comprehend for intelligent document processing. Build document ingestion, preprocessing, and data transformation pipelines. Implement vector database solutions, including document chunking, embedding generation, and vector indexing. Integrate AI agents with Retrieval-Augmented Generation (RAG) pipelines and enterprise knowledge bases. Optimize retrieval performance and contextual accuracy. 6. CI/CD & Deployment Automation
Build and maintain CI/CD pipelines using GitHub Actions. Automate agent code builds, testing, deployment, and configuration management. Implement deployment workflows for Amazon Bedrock AgentCore Runtime. Integrate automated agent evaluations into CI/CD processes. Apply DevOps best practices for version control, release management, and production monitoring. Required Technical Skills
Programming:
Strong Python development experience. Agentic AI:
Claude Agent SDK, AI agent development, agent orchestration, tool calling. AWS AI Services:
Amazon Bedrock, Bedrock AgentCore Runtime, Step Functions, Textract, Comprehend. Observability:
Langfuse deployment, LLM tracing, monitoring, performance analysis. Evaluation:
Continuous agent evaluation, LLM-as-a-Judge, evaluation metrics and automated testing. DevOps:
GitHub Actions, CI/CD pipelines, Git, deployment automation. RAG:
Vector databases, embeddings, chunking strategies, indexing, knowledge base integrations. Cloud:
AWS architecture, IAM, CloudWatch, security, and deployment practices. Good-to-Have Skills
Experience with LangChain, LangGraph, or other agent orchestration frameworks. Experience with RAGAS, DeepEval, or similar LLM evaluation frameworks. Familiarity with Amazon OpenSearch, Pinecone, FAISS, or pgvector. Experience with Docker, Terraform, or infrastructure as code. Knowledge of Model Context Protocol (MCP). Experience with prompt engineering, guardrails, and AI governance. Experience deploying enterprise Generative AI solutions in production environments. Qualifications
6-8+ years of software engineering, cloud engineering, or AI/ML development experience. Strong hands-on experience building Generative AI or Agentic AI applications. Demonstrated experience deploying and supporting AI agents in production. Strong understanding of AWS cloud services, LLM architectures, and automated deployment processes. Ability to independently design, implement, troubleshoot, and optimize AI agent solutions.
MatchPoint Solutions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
IT-Engineering services company with clients across the US . We provide technology solutions to various clients like
Uber, Robinhood, Netflix, Airbnb, Google, Sephora, and more!
More recently, we have expanded to working internationally in
Canada, China, Ireland, UK, Brazil, and India . Through our culture of innovation, we inspire, build, and deliver business results, from idea to outcome. We keep our clients on the cutting edge of the latest technologies and provide solutions by using industry-specific best practices and expertise.
We are excited to be continuously expanding our team. If you are interested in this position, please send over your updated resume. We look forward to hearing from you!
Job Title:
AI Engineer (Agentic AI / AWS Bedrock)
Location: (Remote / Hybrid / On-site - City, Country) Los Angeles,CA - Hybrid
Employment Type: (Full-time / Part-time / Contract) - Contract
Pay Range: 70-80/hr
We are seeking an experienced
AI Engineer
with strong 6-8 yrs experience. Hands-on expertise in building, deploying, and monitoring production-grade AI agents and Generative AI solutions using AWS technologies.
The ideal candidate will have experience developing AI agents using
Claude Agent SDK , deploying them on
Amazon Bedrock AgentCore Runtime , automating deployments through
GitHub Actions , and implementing
Langfuse for observability and continuous evaluation of agent traffic .
The role requires a strong combination of AI engineering, cloud deployment, data pipeline development, and LLM performance monitoring.
Key Responsibilities
1. Agentic AI Development
Design, develop, and deploy AI agents using Claude Agent SDK and Python. Build intelligent, autonomous agents capable of executing complex workflows and interacting with external tools, APIs, and knowledge bases. Develop agent orchestration workflows with appropriate error handling, retry mechanisms, and guardrails. Integrate AI agents with enterprise applications and data sources. 2. AWS Bedrock & AgentCore
Deploy and manage AI agents using Amazon Bedrock AgentCore Runtime. Develop scalable, secure, and production-ready agent architectures on AWS. Integrate agents with Amazon Bedrock foundation models and enterprise knowledge bases. Implement agent authentication, access controls, logging, and runtime monitoring. 3. Langfuse Deployment & AI Observability
Deploy, configure, and maintain Langfuse for comprehensive LLM and agent observability. Integrate Langfuse with AI agents to capture traces, prompts, responses, tool calls, and execution metrics. Monitor agent performance, response latency, token consumption, and operational costs. Identify and troubleshoot production issues through distributed tracing and monitoring dashboards. 4. Continuous Agent Evaluation
Design and implement continuous evaluation frameworks for production AI agent traffic. Evaluate agent responses for accuracy, relevance, hallucinations, task completion, and tool execution quality. Develop automated evaluation pipelines using Langfuse, LLM-as-a-Judge, and custom evaluation metrics. Implement continuous feedback mechanisms to improve agent reliability and effectiveness. Establish quality benchmarks and detect agent performance regressions. 5. Data Pipelines & Knowledge Base Integration
Develop AWS Step Functions workflows integrating Amazon Textract and Amazon Comprehend for intelligent document processing. Build document ingestion, preprocessing, and data transformation pipelines. Implement vector database solutions, including document chunking, embedding generation, and vector indexing. Integrate AI agents with Retrieval-Augmented Generation (RAG) pipelines and enterprise knowledge bases. Optimize retrieval performance and contextual accuracy. 6. CI/CD & Deployment Automation
Build and maintain CI/CD pipelines using GitHub Actions. Automate agent code builds, testing, deployment, and configuration management. Implement deployment workflows for Amazon Bedrock AgentCore Runtime. Integrate automated agent evaluations into CI/CD processes. Apply DevOps best practices for version control, release management, and production monitoring. Required Technical Skills
Programming:
Strong Python development experience. Agentic AI:
Claude Agent SDK, AI agent development, agent orchestration, tool calling. AWS AI Services:
Amazon Bedrock, Bedrock AgentCore Runtime, Step Functions, Textract, Comprehend. Observability:
Langfuse deployment, LLM tracing, monitoring, performance analysis. Evaluation:
Continuous agent evaluation, LLM-as-a-Judge, evaluation metrics and automated testing. DevOps:
GitHub Actions, CI/CD pipelines, Git, deployment automation. RAG:
Vector databases, embeddings, chunking strategies, indexing, knowledge base integrations. Cloud:
AWS architecture, IAM, CloudWatch, security, and deployment practices. Good-to-Have Skills
Experience with LangChain, LangGraph, or other agent orchestration frameworks. Experience with RAGAS, DeepEval, or similar LLM evaluation frameworks. Familiarity with Amazon OpenSearch, Pinecone, FAISS, or pgvector. Experience with Docker, Terraform, or infrastructure as code. Knowledge of Model Context Protocol (MCP). Experience with prompt engineering, guardrails, and AI governance. Experience deploying enterprise Generative AI solutions in production environments. Qualifications
6-8+ years of software engineering, cloud engineering, or AI/ML development experience. Strong hands-on experience building Generative AI or Agentic AI applications. Demonstrated experience deploying and supporting AI agents in production. Strong understanding of AWS cloud services, LLM architectures, and automated deployment processes. Ability to independently design, implement, troubleshoot, and optimize AI agent solutions.
MatchPoint Solutions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.